The present invention relates generally to inspection of semiconductor devices, such as test structures and other types of semiconductor structures. More specifically, it relates to techniques for classifying defects found on integrated circuit devices.
Semiconductor defects may include structural flaws, residual process material and other surface contamination which occur during the production of semiconductor wafers. Defects are typically detected by a class of instruments called inspection tools. Such instruments automatically scan wafer surfaces and detect, and record the location of anomalies using a variety of techniques. This information, or “defect map,” is stored in a computer file and sent to a defect review station.
Using the defect map to locate each defect, a human operator observes each defect under a microscope and classifies each defect according to class (e.g., particle, pit, scratch, or contaminant). Information gained from this process is used to correct the source of defects, and thereby improve the efficiency and yield of the semiconductor production process. Problems with this classification method include the technician's subjectivity in identifying the defect class, and the fatigue associated with the highly repetitive task of observing and classifying these defects.
Methods of automatically classifying defects, collectively known as Automatic Defect Classification or “ADC,” have been developed to overcome the disadvantages of manual defect classification. A conventional ADC system uses image processing techniques to first detect the defect and then to classify the defect according to the defect's physical characteristics and background geometry. Comparing these physical characteristics to the physical characteristics of pre-classified defects in a training set permits automated defect classification.
While this system reduces technician fatigue and increases the number of defects that can be classified per unit time once the training set has been generated, such training set programs sometimes fail to provide an accurate classification for some defects. The setup of the training set typically is time consuming because it requires the manual classification of thousands of defects. During this manual classification process, the user is often presented with thousands of substantially similar defects which require individual manual classification. Needless to say, this process requires a significant amount of man-hours for a user to set up the training set.
Accordingly, there is a need for improved mechanisms for more efficiently setting up an automatic defect classification system. Additionally, there is a need for optimizing and efficiently maintaining an existing classification system.